Motor Unit Discharge Rates during Maximal Shortening and Lengthening Contractions at Three Angular Velocities in Human Elbow Extensors
Bibliographic record
Abstract
INTRODUCTION: Motor unit discharge rates (MUDR) have been reported to be lower during lengthening compared with shortening contractions, but these comparisons have been limited to submaximal, relatively slow angular velocity contractions. The purpose of this study was to evaluate MUDRs from the triceps brachii during maximal-effort shortening and lengthening contractions across multiple angular velocities. METHODS: Young males ( n = 10) and females ( n = 5) completed maximal isokinetic shortening and lengthening elbow extensions at 30, 60, and 90 deg·s -1 through a 60° joint range of motion. Fine-wire electrodes were inserted into the triceps brachii for recording MUDR. Surface electromyography was recorded in a monopolar setup from the triceps brachii. RESULTS: In total, 302 and 335 motor unit trains were analyzed during the shortening and lengthening phases, respectively. No differences ( P = 0.686) in mean MUDRs were observed for the shortening and lengthening phases at 30 deg·s -1 (36.4 ± 11.7 vs 34.4 ± 14.2 Hz), 60 deg·s -1 (38.9 ± 14.2 vs 39.5 ± 13.3 Hz), and 90 deg·s -1 (41.3 ± 12.5 vs 41.6 ± 11.7 Hz). In agreement, surface electromyography amplitude showed no difference between shortening and lengthening contractions across all angular velocities ( P = 0.173). MUDRs were lower at 30 deg·s -1 compared with 60 and 90 deg·s -1 ( P < 0.001), but no differences were present between 60 and 90 deg·s -1 ( P = 0.539). CONCLUSIONS: Contrary to prior comparisons at submaximal levels, we report no differences in MUDRs during maximal shortening and lengthening contractions, indicating no deficit in motor unit activation between dynamic contraction phases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".